40 Algorithms Every Programmer Should Know

Avoiding Masquerading

Attacker X pretends to be an authorized user, Bob, and gains access to sensitive data, which is the trained model, in this case. We need to protect the model against any unauthorized changes.

One way of protecting our trained model against masquerading is by encrypting the model with an authorized user's private key. Once encrypted, anyone can read and utilize the model by decrypting it through the public key of the authorized user, which is found in their digital certificate. No one can make any unauthorized changes to the model.